AIMET Visualization for Quantization API

Top-level API Quantization

aimet_torch.visualize_model.visualize_relative_weight_ranges_to_identify_problematic_layers(model, results_dir, selected_layers=None)[source]

For each of the selected layers, publishes a line plot showing weight ranges for each layer, summary statistics for relative weight ranges, and a histogram showing weight ranges of output channels with respect to the minimum weight range.

Parameters:

model (Module) – pytorch model
results_dir (str) – Directory to save the Bokeh plots
selected_layers (Optional[List]) – a list of layers a user can choose to have visualized. If selected layers is None, all Linear and Conv layers will be visualized.

Return type:

List[figure]

Returns:

A list of bokeh plots

aimet_torch.visualize_model.visualize_weight_ranges(model, results_dir, selected_layers=None)[source]

Visualizes weight ranges for each layer through a scatter plot showing mean plotted against the standard deviation, the minimum plotted against the max, and a line plot with min, max, and mean for each output channel.

Parameters:

model (Module) – pytorch model
selected_layers (Optional[List]) – a list of layers a user can choose to have visualized. If selected layers is None, all Linear and Conv layers will be visualized.
results_dir (str) – Directory to save the Bokeh plots

Return type:

List[figure]

Returns:

A list of bokeh plots

aimet_torch.visualize_model.visualize_changes_after_optimization(old_model, new_model, results_dir, selected_layers=None)[source]

Visualizes changes before and after some optimization has been applied to a model.

Parameters:

old_model (Module) – pytorch model before optimization
new_model (Module) – pytorch model after optimization
results_dir (str) – Directory to save the Bokeh plots
selected_layers (Optional[List]) – a list of layers a user can choose to have visualized. If selected layers is None, all Linear and Conv layers will be visualized.

Return type:

List[figure]

Returns:

A list of bokeh plots

Code Examples

Required imports

import copy
import torch

from torchvision import models

from aimet_torch.cross_layer_equalization import equalize_model

from aimet_torch import batch_norm_fold
from aimet_torch import visualize_model

Comparing Model After Optimization

def visualize_changes_in_model_after_and_before_cle():
    """
    Code example for visualizating model before and after Cross Layer Equalization optimization
    """
    model = models.resnet18(pretrained=True).to(torch.device('cpu'))
    model = model.eval()
    # Create a copy of the model to visualize the before and after optimization changes
    model_copy = copy.deepcopy(model)

    # Specify a folder in which the plots will be saved
    results_dir = './visualization'

    batch_norm_fold.fold_all_batch_norms(model_copy, (1, 3, 224, 224))

    equalize_model(model, (1, 3, 224, 224))
    visualize_model.visualize_changes_after_optimization(model_copy, model, results_dir)

Visualizing weight ranges in Model

def visualize_weight_ranges_model():
    """
    Code example for model visualization
    """
    model = models.resnet18(pretrained=True).to(torch.device('cpu'))
    model = model.eval()

    # Specify a folder in which the plots will be saved
    results_dir = './visualization'

    batch_norm_fold.fold_all_batch_norms(model, (1, 3, 224, 224))

    # Usually it is observed that if we do BatchNorm fold the layer's weight range increases.
    # This helps in visualizing layer's weight
    visualize_model.visualize_weight_ranges(model, results_dir)

Visualizing Relative weight ranges in Model

def visualize_relative_weight_ranges_model():
    """
    Code example for model visualization
    """
    model = models.resnet18(pretrained=True).to(torch.device('cpu'))
    model = model.eval()

    # Specify a folder in which the plots will be saved
    results_dir = './visualization'

    batch_norm_fold.fold_all_batch_norms(model, (1, 3, 224, 224))

    # Usually it is observed that if we do BatchNorm fold the layer's weight range increases.
    # This helps in finding layers which can be equalized to get better performance on hardware
    visualize_model.visualize_relative_weight_ranges_to_identify_problematic_layers(model, results_dir)